It is increasingly apparent that cancer is a heterogeneous disease with a spectrum of mutations present in individual minor clones that aren't represented in the bulk of the tumor cells. The overall hypothesis is that tumor heterogeneity has important clinical implications for treatment sensitivity and acquired resistance to targeted therapies, using EGFR inhibitors in colorectal cancer as a model system. The introduction of more sophisticated techniques for gene sequencing has made possible the identification of low frequency mutations that were missed with standard sequencing techniques. As KRAS mutations represent a well-recognized and clinically utilized predictive biomarker, it represents an opportunity to explore the implications of KRAS heterogeneity in the setting of EGFR monoclonal antibody (mAb) therapy. Recently, we and others have shown that the presence of low frequency KRAS mutations has been detected in tissue from approximately 15% of metastatic colorectal cancer (mCRC) patients when high-sensitivity sequencing is utilized. These rare KRAS mutant cells are present in what are usually identified as KRAS wild type tumor by less sensitive standard-of-care testing methodologies currently used in the clinic, thereby leading to the hypothesis that these clones are rapidly selected under treatment pressure, resulting in clinical progression to treatment. Retrospective, nonrandomized datasets support this hypothesis; these low-frequency KRAS mutations have been correlated with resistance to anti-EGFR treatment. Specifically, further selection of patients eligible for EGFR mAb therapy would reduce the cost of such therapies to the health care system (with modeled savings of $200 million per year given the prospective identification of 25% of non-responding patients). In this proposal we hypothesize that treatment-induced clonal changes are a dynamic balance in a heterogeneous tumor population. We propose to evaluate tumor genetic changes by studying samples from large randomized trials, including the primary tumor and circulating free tumor DNA (cfDNA) obtained from patient plasma samples. To capture longer-term dynamics, we will apply these monitoring techniques to a longitudinal cohort with an embedded prospective clinical trial of anti-EGFR retreatment. Patient-derived murine xenograft models will be utilized to more precisely quantify tumor dynamics. Understand the mechanisms of resistance of CRC will require a more detailed understanding of the heterogeneity and temporal dynamics of genomic changes, thereby leading to improved biomarkers for benefit and novel strategies to re-challenge tumors with previously effective therapy.